import pandas as pd
import numpy as np
import yfinance as yf
import random
from matplotlib import pyplot as plt
import seaborn as sns
import warnings
import secrets
import plotly as plotly
import plotly.express as px
from tqdm import tqdm
import plotly.graph_objects as go
pd.set_option('plotting.backend', 'matplotlib')
plotly.offline.init_notebook_mode()
#warnings.simplefilter(action='ignore', category=FutureWarning)

TICKER="IWDA.AS"
NUMBER_OF_SIMULATIONS=1000
STARTING_CAPITAL = 10000
YEARS_OF_SIMULATION=30

YEARS_OF_SIMULATION=YEARS_OF_SIMULATION+1
Market_Days=253

df=yf.download(TICKER)["Adj Close"].pct_change(1).dropna()

[*********************100%%**********************]  1 of 1 completed

df.plot()

<Axes: xlabel='Date'>

df

Date
2009-09-28    0.000000
2009-09-29    0.000000
2009-09-30    0.000000
2009-10-01    0.000000
2009-10-02    0.000000
                ...   
2023-11-13    0.007003
2023-11-14    0.006568
2023-11-15    0.003582
2023-11-16   -0.005737
2023-11-17    0.003590
Name: Adj Close, Length: 3624, dtype: float64

df_simulations=np.zeros((YEARS_OF_SIMULATION,NUMBER_OF_SIMULATIONS))
df_simulations[0,:]=STARTING_CAPITAL

for x in tqdm(range(0,NUMBER_OF_SIMULATIONS)):
    for i in range(1,YEARS_OF_SIMULATION):
        annual_change=1
        for k in range(Market_Days):
            annual_change=annual_change*(1+(secrets.choice(df.iloc[:])))
        df_simulations[i,x]=annual_change*df_simulations[i-1,x]
df_simulations= pd.DataFrame(df_simulations)
df_simulations_1=  pd.DataFrame(df_simulations)

  0%|          | 0/1000 [00:00<?, ?it/s]C:\Users\dadoi\anaconda3\Lib\random.py:374: FutureWarning:

Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`

100%|██████████| 1000/1000 [03:40<00:00,  4.54it/s]

df_simulations_1

df_simulations=df_simulations_1
df_simulations.iloc[YEARS_OF_SIMULATION-1]

0      418082.037737
1       60542.893262
2      356694.481971
3      239368.524153
4      707771.602070
           ...      
995     55270.776387
996    232582.694463
997     53892.873144
998     79538.480388
999     71958.752289
Name: 30, Length: 1000, dtype: float64

quantile=df_simulations.iloc[YEARS_OF_SIMULATION-1].quantile([0.1,0.9])
quantile=list(quantile)
print(quantile)
df_simulations_purged=df_simulations[df_simulations.columns[ df_simulations.max() < quantile[1]]]
df_simulations_purged=df_simulations_purged[df_simulations_purged.columns[ df_simulations_purged.max() > quantile[0]]]

[82534.57219861187, 716018.8359028095]

df_simulations_purged.plot(figsize=(16,8), title=f"Simulation of {NUMBER_OF_SIMULATIONS} portfolios", legend=False)

<Axes: title={'center': 'Simulation of 1000 portfolios'}>

df_simulations_purged.iloc[YEARS_OF_SIMULATION-1].plot.density(figsize=(16,8),fontsize=14, xlim=(-100000,900000))

<Axes: ylabel='Density'>

top_25 =[]
low_25 =[]
median=[]
for i in range(0,YEARS_OF_SIMULATION):
    top_25.append(df_simulations.iloc[i].quantile(0.75))
    low_25.append(df_simulations.iloc[i].quantile(0.25))
    median.append(df_simulations.iloc[i].median())
columns=["top 25%","median","bottom 25%"]
df_statistics=pd.DataFrame(list(zip(top_25,median,low_25)),columns=columns)
df_statistics

#ax= df_statistics.plot(legend=None,logy=False,fontsize=20,figsize=(25,15),linewidth=4,color="black",title=f"Simulation of {NUMBER_OF_SIMULATIONS} portfolios")
pd.set_option('plotting.backend', 'plotly')

fig = df_statistics.plot(width=1600, height=800,title=f"Simulation of {NUMBER_OF_SIMULATIONS} portfolios",labels= {"index": "Years to Maturity", "value":"Total Capital"} )
fig.add_trace(go.Scatter(x=list(range(0,YEARS_OF_SIMULATION)),y=np.array(top_25),fill='tonexty',mode='lines', line_color='blue' , fillcolor ="red", showlegend=False, hoverinfo="skip"))
fig.add_trace(go.Scatter(x=list(range(0,YEARS_OF_SIMULATION)),y=np.array(median),fill='tonexty',mode='lines', line_color='orange', fillcolor="green", showlegend=False, hoverinfo="skip"))

ls_simulations_at_profit=[]
for i in range(YEARS_OF_SIMULATION):
    c=len([1 for i in list(df_simulations.iloc[i])if i > STARTING_CAPITAL])
    ls_simulations_at_profit.append(c/NUMBER_OF_SIMULATIONS*100)
pd.set_option('plotting.backend', 'plotly')
df_simulations_at_profit = pd.DataFrame(ls_simulations_at_profit,columns=["Profit %"])
df_simulations_at_profit.plot(title="Minimum investment horizon", labels= {"index": "Period length in years", "value":"Chance to be in profit"})

TICKER="^GSPC"

df=yf.download(TICKER)["Adj Close"].pct_change(1).dropna()
df_simulations = np.zeros((YEARS_OF_SIMULATION, NUMBER_OF_SIMULATIONS))
df_simulations[0, :] = STARTING_CAPITAL

for x in tqdm(range(0, NUMBER_OF_SIMULATIONS)):
    for i in range(1, YEARS_OF_SIMULATION):
        annual_change = 1
        for k in range(Market_Days):
            annual_change = annual_change * (1 + (secrets.choice(df.iloc[:])))
        df_simulations[i, x] = annual_change * df_simulations[i - 1, x]
df_simulations = pd.DataFrame(df_simulations)
df_simulations_1 = pd.DataFrame(df_simulations)
df_simulations = df_simulations_1
df_simulations.iloc[YEARS_OF_SIMULATION - 1]
quantile = df_simulations.iloc[YEARS_OF_SIMULATION - 1].quantile([0.1, 0.9])
quantile = list(quantile)
df_simulations_purged = df_simulations[df_simulations.columns[df_simulations.max() < quantile[1]]]
df_simulations_purged = df_simulations_purged[df_simulations_purged.columns[df_simulations_purged.max() > quantile[0]]]
top_25 =[]
low_25 =[]
median=[]
for i in range(0,YEARS_OF_SIMULATION):
    top_25.append(df_simulations.iloc[i].quantile(0.75))
    low_25.append(df_simulations.iloc[i].quantile(0.25))
    median.append(df_simulations.iloc[i].median())
columns=["top 25%","median","bottom 25%"]
df_statistics=pd.DataFrame(list(zip(top_25,median,low_25)),columns=columns)
pd.set_option('plotting.backend', 'plotly')

fig = df_statistics.plot(width=1600, height=800,title=f"Simulation of {NUMBER_OF_SIMULATIONS} portfolios",labels= {"index": "Years to Maturity", "value":"Total Capital"} )
fig.add_trace(go.Scatter(x=list(range(0,YEARS_OF_SIMULATION)),y=np.array(top_25),fill='tonexty',mode='lines', line_color='blue' , fillcolor ="red", showlegend=False, hoverinfo="skip"))
fig.add_trace(go.Scatter(x=list(range(0,YEARS_OF_SIMULATION)),y=np.array(median),fill='tonexty',mode='lines', line_color='orange', fillcolor="green", showlegend=False, hoverinfo="skip"))

[*********************100%%**********************]  1 of 1 completed

  0%|          | 0/1000 [00:00<?, ?it/s]C:\Users\dadoi\anaconda3\Lib\random.py:374: FutureWarning:

Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`

100%|██████████| 1000/1000 [03:45<00:00,  4.44it/s]

ls_simulations_at_profit = []
for i in range(YEARS_OF_SIMULATION):
    c = len([1 for i in list(df_simulations.iloc[i]) if i > STARTING_CAPITAL])
    ls_simulations_at_profit.append(c / NUMBER_OF_SIMULATIONS * 100)

pd.set_option('plotting.backend', 'plotly')
df_simulations_at_profit = pd.DataFrame(ls_simulations_at_profit, columns=["Profit %"])
df_simulations_at_profit.plot(title="Minimum investment horizon",
                              labels={"index": "Period length in years", "value": "Chance to be in profit"})

!jupyter nbconvert --execute --to html Portfolio1.ipynb --HTMLExporter.theme=dark

[NbConvertApp] Converting notebook Portfolio1.ipynb to html
0.00s - Debugger warning: It seems that frozen modules are being used, which may
0.00s - make the debugger miss breakpoints. Please pass -Xfrozen_modules=off
0.00s - to python to disable frozen modules.
0.00s - Note: Debugging will proceed. Set PYDEVD_DISABLE_FILE_VALIDATION=1 to disable this validation.
0.00s - Debugger warning: It seems that frozen modules are being used, which may
0.00s - make the debugger miss breakpoints. Please pass -Xfrozen_modules=off
0.00s - to python to disable frozen modules.
0.00s - Note: Debugging will proceed. Set PYDEVD_DISABLE_FILE_VALIDATION=1 to disable this validation.
[NbConvertApp] WARNING | Alternative text is missing on 3 image(s).
[NbConvertApp] Writing 4927937 bytes to Portfolio1.html

	0	1	2	3	4	5	6	7	8	9	...	990	991	992	993	994	995	996	997	998	999
0	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	1.000000e+04	...	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000
1	14391.081342	11114.594445	13641.291286	9881.361527	13715.146132	10938.456404	12106.250309	12643.757071	8414.515391	9.735893e+03	...	11716.240008	12427.809658	9433.014015	11272.485106	12666.208914	8974.320050	10014.322864	9807.916615	14193.061726	11656.428927
2	16130.113737	10475.786251	16107.070491	10758.858329	10057.337867	9810.825993	14613.786036	11286.743381	8201.234776	1.186860e+04	...	7456.187862	16962.159736	13217.642232	11743.502048	12689.933473	9848.893745	11761.756215	10960.041252	15907.350613	13649.305876
3	18517.003910	12808.610213	16419.085334	10998.625963	11015.644701	9410.429605	15738.362147	10385.358761	11313.460841	1.432929e+04	...	7791.260736	16416.793010	14351.927263	12214.794132	14948.984609	7746.853485	13584.728811	12061.375163	25643.256510	11961.382544
4	18020.248771	12373.940253	25690.599278	14535.229288	13174.192627	10516.847626	21192.141447	11535.502295	13618.504677	1.591157e+04	...	10504.846571	22042.255562	18545.109935	11979.703454	22830.151981	8204.095781	14708.787704	13504.840050	24095.218591	13827.538656
5	21924.624980	13308.248446	29845.095256	19767.589023	14962.417247	10694.591427	20985.420823	11103.251570	18873.428626	1.782794e+04	...	10332.504447	21683.480293	20949.532622	15162.501488	27603.283484	10663.590380	16408.676711	13938.971979	27248.421675	14246.173522
6	21070.242327	12014.352130	34550.928466	25235.545180	17791.314170	14203.006616	18532.469790	10213.886808	21315.319722	2.230538e+04	...	12828.101404	22156.033697	25861.429186	19388.423492	20806.145678	14722.081066	14372.685765	17247.445657	27224.419343	13350.295521
7	22604.658564	8389.936012	36663.136304	26417.085863	18576.462867	14271.647381	20430.048323	9830.382957	23980.392271	2.553408e+04	...	12260.883188	28361.196000	28241.068225	22777.109031	24611.707381	21071.418346	17070.924706	20786.679115	28639.920657	18827.835298
8	27062.204610	8295.906003	42580.282738	37620.454641	21376.945670	18788.082562	18890.695548	9206.251158	28757.143986	3.866121e+04	...	10429.686127	27479.257742	34130.220986	28010.581223	21142.072925	18250.507164	21392.495403	17290.043930	31412.461206	20054.313158
9	28179.805692	7299.430220	50601.139400	43440.134283	27803.360850	21324.046333	22559.149433	9023.938917	35313.850430	4.754991e+04	...	14289.601389	31405.137987	32716.017255	26028.254690	30169.191247	22653.840223	20773.656763	16996.295109	28260.375393	24336.291799
10	34492.035383	10849.521907	53621.371071	43891.975494	28177.188733	28003.800019	23954.276565	9729.826587	44760.713631	4.504998e+04	...	12189.513331	32062.563053	43078.267676	23633.821085	26890.746135	23119.209315	22296.577053	19334.549936	37494.133804	27062.647844
11	38401.282767	15521.890573	53012.111839	42935.542604	29269.365426	28793.695733	31421.839707	9392.924750	51441.877325	4.638398e+04	...	8756.081204	44990.786904	54492.536564	22978.594954	34748.687403	18454.239384	28076.670171	25424.759143	35390.499022	43508.218262
12	40388.250248	19641.707369	61856.491530	43335.418187	34142.189569	40730.775927	46928.191201	10456.234052	65097.416254	6.318272e+04	...	9616.071752	56549.413943	65904.163777	19948.173853	39283.168431	22493.372504	38426.641078	25043.302585	32945.296556	44312.652114
13	51695.118896	19364.686530	84921.127027	48369.247143	38413.031539	47415.210221	48318.893897	12939.764814	87382.921238	9.111321e+04	...	9216.832604	57695.138934	85525.201801	25189.538051	34591.530510	33285.887671	36511.011317	36720.942193	32360.247191	43939.207703
14	70589.330354	21059.303153	111133.515039	53588.179071	57926.924621	62502.983768	62125.888018	16873.607263	101545.053398	1.163115e+05	...	9278.522095	68372.863854	103137.428109	32219.828330	44835.747313	27896.539992	37516.321211	33726.036560	43385.200991	46452.522070
15	79767.621123	22874.381429	107204.688093	58403.459183	67008.917350	75452.687128	75952.932909	18998.729190	93228.391827	1.673835e+05	...	11735.879794	70407.001920	124655.738733	31515.497218	61259.422974	36541.862042	42820.158655	40069.814222	37527.099798	57514.024914
16	91957.096639	29229.658482	145063.353687	71040.719539	70973.092936	81178.133056	87220.351978	22921.786967	99165.531630	1.585271e+05	...	14404.756843	93072.967837	120277.912821	33741.915133	76642.285000	24284.999561	47082.936662	38817.022568	35698.263707	65680.299463
17	119795.695588	21674.359433	161522.012418	100811.592668	91841.700153	108611.499428	98248.105664	29497.010720	110887.738120	1.471038e+05	...	20278.976967	99280.687098	99381.235667	33845.445043	69141.680534	26229.556389	58685.367198	49979.817121	43875.194982	65040.418276
18	165455.115254	28185.552612	159515.107179	118462.250298	128249.399225	149923.433388	120292.287227	34439.577367	120807.033938	1.501707e+05	...	27225.377846	91670.949231	112431.748571	49901.582036	92563.303045	23673.418014	51300.184355	60936.032430	60185.907820	64339.162068
19	219592.759175	28505.545059	121528.423203	142297.125693	131944.533434	131515.743516	117401.187766	47550.444583	121732.112627	1.389665e+05	...	33994.599100	116551.401968	122700.612029	41852.724307	92995.528816	24924.575850	48470.090780	65511.724822	75702.405200	67528.803462
20	215354.838921	28219.264428	161525.015653	176220.070821	115184.156487	140446.778599	127578.594503	56792.870630	123352.113300	1.371886e+05	...	41132.167634	121131.097146	81429.307937	43973.974240	100653.104014	25572.185398	51868.139808	49191.524375	66483.780374	77704.175070
21	222097.947219	34130.026123	136486.399854	191726.555497	118820.378311	149754.667804	137340.061330	86692.365808	116677.582977	1.591051e+05	...	39960.603827	125307.407013	91198.680618	46046.246297	127026.972556	33572.029416	62477.998320	51028.055754	86790.892929	63602.489366
22	194486.805097	28026.507454	138367.878628	254981.458065	156970.753256	187178.185051	136718.637420	99233.080738	134228.967187	1.849489e+05	...	47892.603134	178470.185996	113247.138745	45423.164927	148455.740501	32200.375294	89602.024409	43024.600636	104648.235503	72638.594323
23	238926.576856	29081.422160	113415.200519	228501.346170	257753.829497	191781.809190	110540.032542	78852.913091	158565.439875	2.036813e+05	...	56013.867722	192710.242704	101886.040897	52865.159515	167138.106877	33266.257217	113155.791874	42885.094504	114886.980383	66703.154711
24	266271.698565	33691.946978	128069.951504	272655.712578	314313.772958	190768.375152	108136.618072	113254.822279	134666.861782	2.709906e+05	...	80136.363304	219371.449975	149103.606736	58354.129893	229208.503250	38996.736255	140369.859445	51566.829317	148600.413077	57137.947652
25	255014.759679	37631.301626	146283.594802	297990.906780	375749.883839	219217.345005	98475.841052	125403.598004	161603.970316	4.172211e+05	...	91242.223462	190143.149394	183459.743954	65956.993301	261805.645665	38705.335258	162002.964529	55802.215027	128696.435274	56311.739051
26	333396.784268	39600.201361	190633.232682	285438.841073	397662.354085	237769.803644	134949.596355	127493.965833	177434.787392	4.903390e+05	...	108028.930586	253336.377045	215810.338110	87206.691368	276087.244450	57297.461661	150148.561534	48010.018141	105316.111713	68710.743522
27	461894.002759	38454.174742	160094.237016	271785.303300	421831.750545	278397.915267	169785.368530	152785.540682	160008.655167	6.178826e+05	...	115679.608378	210550.101001	235706.284998	91939.682945	367835.484835	60125.475841	146520.203596	48664.154358	93487.981795	82050.567542
28	519955.299543	49079.304070	205168.448963	219731.174581	626391.181500	262877.990409	201464.033755	153545.926454	170491.705550	6.644619e+05	...	109002.127212	219900.651995	261699.535002	100031.455778	334494.765461	71894.027943	180102.842290	52401.448453	79995.776966	86038.355802
29	543541.917558	49111.938166	240771.324285	220711.115025	868930.756496	346879.143349	180399.410474	178250.103358	209612.759010	9.994884e+05	...	155120.877343	213303.801169	294635.196383	106487.918561	325997.539813	64674.681912	202189.100455	57296.432173	83855.305641	74762.269719
30	418082.037737	60542.893262	356694.481971	239368.524153	707771.602070	302819.641690	207786.977976	184980.034036	211401.761116	1.266303e+06	...	187903.730103	282537.960057	289855.930362	130017.711989	391643.060765	55270.776387	232582.694463	53892.873144	79538.480388	71958.752289

	top 25%	median	bottom 25%
0	10000.000000	10000.000000	10000.000000
1	12354.880704	11132.669780	10043.348226
2	14105.925399	12291.949498	10795.809567
3	16512.198337	13671.663159	11512.163497
4	19056.137038	15095.071631	12353.001476
5	21350.622562	17131.845640	13451.593428
6	24191.801331	18972.385317	14460.734244
7	27745.931265	21180.676971	15614.921626
8	31445.274231	23509.018395	17155.876114
9	35734.119363	26313.466013	18498.240864
10	40785.619685	28740.347624	20432.190417
11	44747.909626	32141.635602	22441.380840
12	51487.323388	36083.786404	24365.684393
13	57306.494821	39810.274302	26554.590258
14	64011.837540	44470.241423	29629.746920
15	72237.518780	49240.656172	32302.556202
16	81145.671268	54457.817297	34174.041084
17	93683.695713	60669.792672	37961.574579
18	104257.767449	66488.957419	41352.408871
19	119520.765472	73723.690049	47127.233414
20	133920.057937	83970.718336	53184.041969
21	149220.400140	92801.449562	58504.174146
22	170567.736789	102440.150613	63306.247199
23	190970.017941	113259.544380	71265.958560
24	215437.738102	127926.802935	76090.161503
25	243712.887210	142154.790020	85166.865803
26	274547.838743	157495.467250	94397.425924
27	305669.981755	175556.065987	107032.427204
28	341618.033606	195672.680595	117489.588020
29	393481.273846	219848.950886	125568.287215
30	431383.713318	248145.966322	141017.982309

PORTFOLIO ONLY IWDA